Systems-level engineering and characterisation of Clostridium autoethanogenum through heterologous production of poly-3-hydroxybutyrate (PHB).

Gas fermentation is emerging as an economically attractive option for the sustainable production of fuels and chemicals from gaseous waste feedstocks. Clostridium autoethanogenum can use CO and/or CO2 + H2 as its sole carbon and energy sources. Fermentation of C. autoethanogenum is currently being deployed on a commercial scale for ethanol production. Expanding the product spectrum of acetogens will enhance the economics of gas fermentation. To achieve efficient heterologous product synthesis, limitations in redox and energy metabolism must be overcome. Here, we engineered and characterised at a systems-level, a recombinant poly-3-hydroxybutyrate (PHB)-producing strain of C. autoethanogenum. Cells were grown in CO-limited steady-state chemostats on two gas mixtures, one resembling syngas (20% H2) and the other steel mill off-gas (2% H2). Results were characterised using metabolomics and transcriptomics, and then integrated using a genome-scale metabolic model reconstruction. PHB-producing cells had an increased expression of the Rnf complex, suggesting energy limitations for heterologous production. Subsequent optimisation of the bioprocess led to a 12-fold increase in the cellular PHB content. The data suggest that the cellular redox state, rather than the acetyl-CoA pool, was limiting PHB production. Integration of the data into the genome-scale metabolic model showed that ATP availability limits PHB production. Altogether, the data presented here advances the fundamental understanding of heterologous product synthesis in gas-fermenting acetogens.

C4GEM, a genome-scale metabolic model to study C4 plant metabolism.

Leaves of C(4) grasses (such as maize [Zea mays], sugarcane [Saccharum officinarum], and sorghum [Sorghum bicolor]) form a classical Kranz leaf anatomy. Unlike C(3) plants, where photosynthetic CO(2) fixation proceeds in the mesophyll (M), the fixation process in C(4) plants is distributed between two cell types, the M cell and the bundle sheath (BS) cell. Here, we develop a C(4) genome-scale model (C4GEM) for the investigation of flux distribution in M and BS cells during C(4) photosynthesis. C4GEM, to our knowledge, is the first large-scale metabolic model that encapsulates metabolic interactions between two different cell types. C4GEM is based on the Arabidopsis (Arabidopsis thaliana) model (AraGEM) but has been extended by adding reactions and transporters responsible to represent three different C(4) subtypes (NADP-ME [for malic enzyme], NAD-ME, and phosphoenolpyruvate carboxykinase). C4GEM has been validated for its ability to synthesize 47 biomass components and consists of 1,588 unique reactions, 1,755 metabolites, 83 interorganelle transporters, and 29 external transporters (including transport through plasmodesmata). Reactions in the common C(4) model have been associated with well-annotated C(4) species (NADP-ME subtypes): 3,557 genes in sorghum, 11,623 genes in maize, and 3,881 genes in sugarcane. The number of essential reactions not assigned to genes is 131, 135, and 156 in sorghum, maize, and sugarcane, respectively. Flux balance analysis was used to assess the metabolic activity in M and BS cells during C(4) photosynthesis. Our simulations were consistent with chloroplast proteomic studies, and C4GEM predicted the classical C(4) photosynthesis pathway and its major effect in organelle function in M and BS. The model also highlights differences in metabolic activities around photosystem I and photosystem II for three different C(4) subtypes. Effects of CO(2) leakage were also explored. C4GEM is a viable framework for in silico analysis of cell cooperation between M and BS cells during photosynthesis and can be used to explore C(4) plant metabolism.

AraGEM, a genome-scale reconstruction of the primary metabolic network in Arabidopsis.

Genome-scale metabolic network models have been successfully used to describe metabolism in a variety of microbial organisms as well as specific mammalian cell types and organelles. This systems-based framework enables the exploration of global phenotypic effects of gene knockouts, gene insertion, and up-regulation of gene expression. We have developed a genome-scale metabolic network model (AraGEM) covering primary metabolism for a compartmentalized plant cell based on the Arabidopsis (Arabidopsis thaliana) genome. AraGEM is a comprehensive literature-based, genome-scale metabolic reconstruction that accounts for the functions of 1,419 unique open reading frames, 1,748 metabolites, 5,253 gene-enzyme reaction-association entries, and 1,567 unique reactions compartmentalized into the cytoplasm, mitochondrion, plastid, peroxisome, and vacuole. The curation process identified 75 essential reactions with respective enzyme associations not assigned to any particular gene in the Kyoto Encyclopedia of Genes and Genomes or AraCyc. With the addition of these reactions, AraGEM describes a functional primary metabolism of Arabidopsis. The reconstructed network was transformed into an in silico metabolic flux model of plant metabolism and validated through the simulation of plant metabolic functions inferred from the literature. Using efficient resource utilization as the optimality criterion, AraGEM predicted the classical photorespiratory cycle as well as known key differences between redox metabolism in photosynthetic and nonphotosynthetic plant cells. AraGEM is a viable framework for in silico functional analysis and can be used to derive new, nontrivial hypotheses for exploring plant metabolism.

A TetR-Family Protein (CAETHG_0459) Activates Transcription From a New Promoter Motif Associated With Essential Genes for Autotrophic Growth in Acetogens.

Acetogens can fix carbon (CO or CO2) into acetyl-CoA via the Wood-Ljungdahl pathway (WLP) that also makes them attractive cell factories for the production of fuels and chemicals from waste feedstocks. Although most biochemical details of the WLP are well understood and systems-level characterization of acetogen metabolism has recently improved, key transcriptional features such as promoter motifs and transcriptional regulators are still unknown in acetogens. Here, we use differential RNA-sequencing to identify a previously undescribed promoter motif associated with essential genes for autotrophic growth of the model-acetogen Clostridium autoethanogenum. RNA polymerase was shown to bind to the new promoter motif using a DNA-binding protein assay and proteomics enabled the discovery of four candidates to potentially function directly in control of transcription of the WLP and other key genes of C1 fixation metabolism. Next, in vivo experiments showed that a TetR-family transcriptional regulator (CAETHG_0459) and the housekeeping sigma factor (σA) activate expression of a reporter protein (GFP) in-frame with the new promoter motif from a fusion vector in Escherichia coli. Lastly, a protein-protein interaction assay with the RNA polymerase (RNAP) shows that CAETHG_0459 directly binds to the RNAP. Together, the data presented here advance the fundamental understanding of transcriptional regulation of C1 fixation in acetogens and provide a strategy for improving the performance of gas-fermenting bacteria by genetic engineering.

Maintenance of ATP Homeostasis Triggers Metabolic Shifts in Gas-Fermenting Acetogens.

Acetogens are promising cell factories for producing fuels and chemicals from waste feedstocks via gas fermentation, but quantitative characterization of carbon, energy, and redox metabolism is required to guide their rational metabolic engineering. Here, we explore acetogen gas fermentation using physiological, metabolomics, and transcriptomics data for Clostridium autoethanogenum steady-state chemostat cultures grown on syngas at various gas-liquid mass transfer rates. We observe that C. autoethanogenum shifts from acetate to ethanol production to maintain ATP homeostasis at higher biomass concentrations but reaches a limit at a molar acetate/ethanol ratio of ∼1. This regulatory mechanism eventually leads to depletion of the intracellular acetyl-CoA pool and collapse of metabolism. We accurately predict growth phenotypes using a genome-scale metabolic model. Modeling revealed that the methylene-THF reductase reaction was ferredoxin reducing. This work provides a reference dataset to advance the understanding and engineering of arguably the first carbon fixation pathway on Earth.